Venous-plexus-associated lymphoid hubs support meningeal humoral immunity.

There is increasing interest in how immune cells in the meninges-the membranes that surround the brain and spinal cord-contribute to homeostasis and disease in the central nervous system1,2. The outer layer of the meninges, the dura mater, has recently been described to contain both innate and adaptive immune cells, and functions as a site for B cell development3-6. Here we identify organized lymphoid structures that protect fenestrated vasculature in the dura mater. The most elaborate of these dural-associated lymphoid tissues (DALT) surrounded the rostral-rhinal confluence of the sinuses and included lymphatic vessels. We termed this structure, which interfaces with the skull bone marrow and a comparable venous plexus at the skull base, the rostral-rhinal venolymphatic hub. Immune aggregates were present in DALT during homeostasis and expanded with age or after challenge with systemic or nasal antigens. DALT contain germinal centre B cells and support the generation of somatically mutated, antibody-producing cells in response to a nasal pathogen challenge. Inhibition of lymphocyte entry into the rostral-rhinal hub at the time of nasal viral challenge abrogated the generation of germinal centre B cells and class-switched plasma cells, as did perturbation of B-T cell interactions. These data demonstrate a lymphoid structure around vasculature in the dura mater that can sample antigens and rapidly support humoral immune responses after local pathogen challenge.

Visual Prompting Based Incremental Learning for Semantic Segmentation of Multiplex Immuno-Flourescence Microscopy Imagery.

Deep learning approaches are state-of-the-art for semantic segmentation of medical images, but unlike many deep learning applications, medical segmentation is characterized by small amounts of annotated training data. Thus, while mainstream deep learning approaches focus on performance in domains with large training sets, researchers in the medical imaging field must apply new methods in creative ways to meet the more constrained requirements of medical datasets. We propose a framework for incrementally fine-tuning a multi-class segmentation of a high-resolution multiplex (multi-channel) immuno-flourescence image of a rat brain section, using a minimal amount of labelling from a human expert. Our framework begins with a modified Swin-UNet architecture that treats each biomarker in the multiplex image separately and learns an initial "global" segmentation (pre-training). This is followed by incremental learning and refinement of each class using a very limited amount of additional labeled data provided by a human expert for each region and its surroundings. This incremental learning utilizes the multi-class weights as an initialization and uses the additional labels to steer the network and optimize it for each region in the image. In this way, an expert can identify errors in the multi-class segmentation and rapidly correct them by supplying the model with additional annotations hand-picked from the region. In addition to increasing the speed of annotation and reducing the amount of labelling, we show that our proposed method outperforms a traditional multi-class segmentation by a large margin.

Metabolic Quadrivalency in RSeT Human Embryonic Stem Cells.

One of the most important properties of human embryonic stem cells (hESCs) is related to their pluripotent states. In our recent study, we identified a previously unrecognized pluripotent state induced by RSeT medium. This state makes primed hESCs resistant to conversion to naïve pluripotent state. In this study, we have further characterized the metabolic features in these RSeT hESCs, including metabolic gene expression, metabolomic analysis, and various functional assays. The commonly reported metabolic modes include glycolysis or both glycolysis and oxidative phosphorylation (i.e., metabolic bivalency) in pluripotent stem cells. However, besides the presence of metabolic bivalency, RSeT hESCs exhibited a unique metabolome with additional fatty acid oxidation and imbalanced nucleotide metabolism. This metabolic quadrivalency is linked to hESC growth independent of oxygen tension and restricted capacity for naïve reprogramming in these cells. Thus, this study provides new insights into pluripotent state transitions and metabolic stress-associated hPSC growth in vitro.

Cellular data extraction from multiplexed brain imaging data using self-supervised Dual-loss Adaptive Masked Autoencoder.

Reliable large-scale cell detection and segmentation is the fundamental first step to understanding biological processes in the brain. The ability to phenotype cells at scale can accelerate preclinical drug evaluation and system-level brain histology studies. The impressive advances in deep learning offer a practical solution to cell image detection and segmentation. Unfortunately, categorizing cells and delineating their boundaries for training deep networks is an expensive process that requires skilled biologists. This paper presents a novel self-supervised Dual-Loss Adaptive Masked Autoencoder (DAMA) for learning rich features from multiplexed immunofluorescence brain images. DAMA's objective function minimizes the conditional entropy in pixel-level reconstruction and feature-level regression. Unlike existing self-supervised learning methods based on a random image masking strategy, DAMA employs a novel adaptive mask sampling strategy to maximize mutual information and effectively learn brain cell data. To the best of our knowledge, this is the first effort to develop a self-supervised learning method for multiplexed immunofluorescence brain images. Our extensive experiments demonstrate that DAMA features enable superior cell detection, segmentation, and classification performance without requiring many annotations. In addition, to examine the generalizability of DAMA, we also experimented on TissueNet, a multiplexed imaging dataset comprised of two-channel fluorescence images from six distinct tissue types, captured using six different imaging platforms. Our code is publicly available at https://github.com/hula-ai/DAMA.

Comparative overview of multi-shell diffusion MRI models to characterize the microstructure of multiple sclerosis lesions and periplaques.

In multiple sclerosis (MS), accurate in vivo characterization of the heterogeneous lesional and extra-lesional tissue pathology remains challenging. Marshalling several advanced imaging techniques - quantitative relaxation time (T1) mapping, a model-free average diffusion signal approach and four multi-shell diffusion models - this study investigates the performance of multi-shell diffusion models and characterizes the microstructural damage within (i) different MS lesion types - active, chronic active, and chronic inactive - (ii) their respective periplaque white matter (WM), and (iii) the surrounding normal-appearing white matter (NAWM). In 83 MS participants (56 relapsing-remitting, 27 progressive) and 23 age and sex-matched healthy controls (HC), we analysed a total of 317 paramagnetic rim lesions (PRL+), 232 non-paramagnetic rim lesions (PRL-), 38 contrast-enhancing lesions (CEL). Consistent with previous findings and histology, our analysis revealed the ability of advanced multi-shell diffusion models to characterize the unique microstructural patterns of CEL, and to elucidate their possible evolution into a resolving (chronic inactive) vs smoldering (chronic active) inflammatory stage. In addition, we showed that the microstructural damage extends well beyond the MRI-visible lesion edge, gradually fading out while moving outward from the lesion edge into the immediate WM periplaque and the NAWM, the latter still characterized by diffuse microstructural damage in MS vs HC. This study also emphasizes the critical role of selecting appropriate diffusion models to elucidate the complex pathological architecture of MS lesions and their periplaque. More specifically, multi-compartment diffusion models based on biophysically interpretable metrics such as neurite orientation dispersion and density (NODDI; mean auc=0.8002) emerge as the preferred choice for MS applications, while simpler models based on a representation of the diffusion signal, like diffusion tensor imaging (DTI; mean auc=0.6942), consistently underperformed, also when compared to T1 mapping (mean auc=0.73375).

scRNA-seq data from the larval Drosophila ventral cord provides a resource for studying motor systems function and development.

The Drosophila larval ventral nerve cord (VNC) shares many similarities with the spinal cord of vertebrates and has emerged as a major model for understanding the development and function of motor systems. Here, we use high-quality scRNA-seq, validated by anatomical identification, to create a comprehensive census of larval VNC cell types. We show that the neural lineages that comprise the adult VNC are already defined, but quiescent, at the larval stage. Using fluorescence-activated cell sorting (FACS)-enriched populations, we separate all motor neuron bundles and link individual neuron clusters to morphologically characterized known subtypes. We discovered a glutamate receptor subunit required for basal neurotransmission and homeostasis at the larval neuromuscular junction. We describe larval glia and endorse the general view that glia perform consistent activities throughout development. This census represents an extensive resource and a powerful platform for future discoveries of cellular and molecular mechanisms in repair, regeneration, plasticity, homeostasis, and behavioral coordination.

Flow cytometric immunophenotypic differentiation patterns of bone marrow eosinophilopoiesis.

BACKGROUND: Flow cytometry has been widely used to study immunophenotypic patterns of maturation of most hematopoietic lineages in normal human bone marrow aspirates, thus allowing identification of changes in patterns in many myeloid malignancies. Eosinophils play an important role in a wide variety of disorders, including some myeloid neoplasms. However, changes in flow cytometric immunophenotypic patterns during normal and abnormal bone marrow eosinophilopoiesis have not been well studied. METHODS: Fresh bone marrow aspirates from 15 healthy donors, 19 patients with hypereosinophilic syndromes (HES), and 11 patients with systemic mastocytosis (SM) were analyzed for candidate markers that included EMR-1, Siglec-8, CCR3, CD9, CD11a, CD11b, CD11c, CD13, CD16, CD29, CD34, CD38, CD45, CD44, CD49d, CD49f, CD54, CD62L, CD69, CD117, CD125 (IL-5Rα), HLA-DR, using 10 parameter flow cytometry. Putative CD34-negative immature and mature normal eosinophil populations were first identified based on changes in expression of the above markers in healthy donors, then confirmed using fluorescence-based cell sorting and morphological evaluation of cytospin preparations. The normal immunophenotypic patterns were then compared to immunophenotypic patterns of eosinophilopoiesis in patients with HES and SM. RESULTS: The eosinophilic lineage was first verified using the human eosinophil-specific antibody EMR-1 in combination with anti-IL-5Rα antibody. Then, a combination of Siglec-8, CD9, CD11b, CCR3, CD49d, and CD49f antibodies was used to delineate normal eosinophilic maturational patterns. Early stages (eosinophilic promyelocytes/myelocytes) were identified as Siglec-8 dim/CD11b dim to moderate/CD9 dim/CCR3 dim/CD49d bright/CD49f dim, intermediate stages (eosinophilic myelocytes/metamyelocytes) as Siglec-8 moderate/CD11b moderate to bright/CD9 moderate/CCR3 moderate/CD49d moderate/CD49f moderate and mature bands/segmented eosinophils as Siglec-8 bright/CD11b bright/CD9 bright/CCR3 bright/CD49d dim/CD49f bright. Overall maturational patterns were also similar in patients with HES and SM; however, the expression levels of several surface markers were altered compared to normal eosinophils. CONCLUSION: A novel flow cytometric antibody panel was devised to detect alterations in immunophenotypic patterns of bone marrow eosinophil maturation and evaluated in normal, HES and SM samples. This approach will allow us to elucidate changes in immunophenotypic patterns of bone marrow eosinophilopoiesis in other hematological diseases.

Analgesic candidate adenosine A3 receptors are expressed by perineuronal peripheral macrophages in human dorsal root ganglion and spinal cord microglia.

ABSTRACT: Adenosine receptors are a family of purinergic G protein-coupled receptors that are widely distributed in bodily organs and in the peripheral and central nervous systems. Recently, antihyperalgesic actions have been suggested for the adenosine A3 receptor, and its agonists have been proposed as new neuropathic pain treatments. We hypothesized that these receptors may be expressed in nociceptive primary afferent neurons. However, RNA sequencing across species, eg, rat, mouse, dog, and human, suggests that dorsal root ganglion (DRG) expression of ADORA3 is inconsistent. In rat and mouse, Adora3 shows very weak to no expression in DRG, whereas it is well expressed in human DRG. However, the cell types in human DRG that express ADORA3 have not been delineated. An examination of DRG cell types using in situ hybridization clearly detected ADORA3 transcripts in peripheral macrophages that are in close apposition to the neuronal perikarya but not in peripheral sensory neurons. By contrast, ADORA1 was found primarily in neurons, where it is broadly expressed at low levels. These results suggest that a more complex or indirect mechanism involving modulation of macrophage and/or microglial cells may underlie the potential analgesic action of adenosine A3 receptor agonism.

Resistance to Naïve and Formative Pluripotency Conversion in RSeT Human Embryonic Stem Cells.

One of the most important properties of human embryonic stem cells (hESCs) is related to their primed and naïve pluripotent states. Our previous meta-analysis indicates the existence of heterogeneous pluripotent states derived from diverse naïve protocols. In this study, we have characterized a commercial medium (RSeT)-based pluripotent state under various growth conditions. Notably, RSeT hESCs can circumvent hypoxic growth conditions as required by naïve hESCs, in which some RSeT cells (e.g., H1 cells) exhibit much lower single cell plating efficiency, having altered or much retarded cell growth under both normoxia and hypoxia. Evidently, hPSCs lack many transcriptomic hallmarks of naïve and formative pluripotency (a phase between naive and primed states). Integrative transcriptome analysis suggests our primed and RSeT hESCs are close to the early stage of post-implantation embryos, similar to the previously reported primary hESCs and early hESC cultures. Moreover, RSeT hESCs did not express naïve surface markers such as CD75, SUSD2, and CD130 at a significant level. Biochemically, RSeT hESCs exhibit a differential dependency of FGF2 and co-independency of both Janus kinase (JAK) and TGFß signaling in a cell-line-specific manner. Thus, RSeT hESCs represent a previously unrecognized pluripotent state downstream of formative pluripotency. Our data suggest that human naïve pluripotent potentials may be restricted in RSeT medium. Hence, this study provides new insights into pluripotent state transitions in vitro.

Syntaxin1A overexpression and pain insensitivity in individuals with 7q11.23 duplication syndrome.

Genetic modifications leading to pain insensitivity phenotypes, while rare, provide invaluable insights into the molecular biology of pain and reveal targets for analgesic drugs. Pain insensitivity typically results from Mendelian loss-of-function mutations in genes expressed in nociceptive (pain-sensing) dorsal root ganglion (DRG) neurons that connect the body to the spinal cord. We document a pain insensitivity mechanism arising from gene overexpression in individuals with the rare 7q11.23 duplication syndrome (Dup7), who have 3 copies of the approximately 1.5-megabase Williams syndrome (WS) critical region. Based on parental accounts and pain ratings, people with Dup7, mainly children in this study, are pain insensitive following serious injury to skin, bones, teeth, or viscera. In contrast, diploid siblings (2 copies of the WS critical region) and individuals with WS (1 copy) show standard reactions to painful events. A converging series of human assessments and cross-species cell biological and transcriptomic studies identified 1 likely candidate in the WS critical region, STX1A, as underlying the pain insensitivity phenotype. STX1A codes for the synaptic vesicle fusion protein syntaxin1A. Excess syntaxin1A was demonstrated to compromise neuropeptide exocytosis from nociceptive DRG neurons. Taken together, these data indicate a mechanism for producing "genetic analgesia" in Dup7 and offer previously untargeted routes to pain control.

TDP-43 proteinopathy in ALS is triggered by loss of ASRGL1 and associated with HML-2 expression.

TAR DNA-binding protein 43 (TDP-43) proteinopathy in brain cells is the hallmark of amyotrophic lateral sclerosis (ALS) but its cause remains elusive. Asparaginase-like-1 protein (ASRGL1) cleaves isoaspartates, which alter protein folding and susceptibility to proteolysis. ASRGL1 gene harbors a copy of the human endogenous retrovirus HML-2, whose overexpression contributes to ALS pathogenesis. Here we show that ASRGL1 expression was diminished in ALS brain samples by RNA sequencing, immunohistochemistry, and western blotting. TDP-43 and ASRGL1 colocalized in neurons but, in the absence of ASRGL1, TDP-43 aggregated in the cytoplasm. TDP-43 was found to be prone to isoaspartate formation and a substrate for ASRGL1. ASRGL1 silencing triggered accumulation of misfolded, fragmented, phosphorylated and mislocalized TDP-43 in cultured neurons and motor cortex of female mice. Overexpression of ASRGL1 restored neuronal viability. Overexpression of HML-2 led to ASRGL1 silencing. Loss of ASRGL1 leading to TDP-43 aggregation may be a critical mechanism in ALS pathophysiology.

Visual Prompting based Incremental Learning for Semantic Segmentation of Multiplex Immuno-Flourescence Microscopy Imagery.

Deep learning approaches are state-of-the-art for semantic segmentation of medical images, but unlike many deep learning applications, medical segmentation is characterized by small amounts of annotated training data. Thus, while mainstream deep learning approaches focus on performance in domains with large training sets, researchers in the medical imaging field must apply new methods in creative ways to meet the more constrained requirements of medical datasets. We propose a framework for incrementally fine-tuning a multi-class segmentation of a high-resolution multiplex (multi-channel) immuno-flourescence image of a rat brain section, using a minimal amount of labelling from a human expert. Our framework begins with a modified Swin-UNet architecture that treats each biomarker in the multiplex image separately and learns an initial "global" segmentation (pre-training). This is followed by incremental learning and refinement of each class using a very limited amount of additional labeled data provided by a human expert for each region and its surroundings. This incremental learning utilizes the multi-class weights as an initialization and uses the additional labels to steer the network and optimize it for each region in the image. In this way, an expert can identify errors in the multi-class segmentation and rapidly correct them by supplying the model with additional annotations hand-picked from the region. In addition to increasing the speed of annotation and reducing the amount of labelling, we show that our proposed method outperforms a traditional multi-class segmentation by a large margin.